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Barbara Hammer, Andrej Gisbrecht, Alexander Hasenfuss,'s Advances in Self-Organizing Maps: 8th International PDF

By Barbara Hammer, Andrej Gisbrecht, Alexander Hasenfuss, Bassam Mokbel (auth.), Jorma Laaksonen, Timo Honkela (eds.)

ISBN-10: 3642215653

ISBN-13: 9783642215650

This e-book constitutes the refereed court cases of the eighth overseas Workshop on Self-Organizing Maps, WSOM 2011, held in Espoo, Finland, in June 2011. The 36 revised complete papers awarded have been conscientiously reviewed and chosen from various submissions. The papers are prepared in topical sections on plenaries; monetary and societal purposes; idea and method; purposes of knowledge mining and research; language processing and rfile research; and visualization and photo processing.

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Additional resources for Advances in Self-Organizing Maps: 8th International Workshop, WSOM 2011, Espoo, Finland, June 13-15, 2011. Proceedings

Example text

This exercise confirms that the Early warning cluster is a less influential cluster that does not add real value to the analysis of the currency crisis cycle. Thus, the 3-cluster model is utilized for assessing the fluctuations in the data. Argentina experienced three crisis episodes during the analyzed period. As shown in Fig. 8, the first crisis in 1975 was preceded by high membership values in the pre-crisis cluster, whereafter the memberships in the crisis and subsequently the tranquil cluster dominated.

The efficiency of the system becomes therefore something that include in a broad sense the bare management of financial variables: for this reason we have analyzed the balance sheets of 300 public enterprises (ASL and AO), as resulting from the more recent Italian Statistics Institute (ISTAT1 ) database. The goal was to retain information that might help to monitor the actual level of efficiency of the National Health System, and, eventually, to find some suggestions to improve it. The data under examination were arranged into two different aggregation levels: regional, and by single unit.

5. It has been shown recently [8] that if this kind of learning with constant neighborhood is used, the batch training algorithm will usually converge in a finite number of cycles. In the present application this kind of fine tuning was continued until no changes in the map vectors took place in further training. This occurred in about 70 cycles. 2 Preliminary Clustering Results In Fig. 1 we have the histograms of four main linguistic classes of the words in the MCRC corpus (restricting to local contexts that do not contain any punctuation marks or other nonlinguistic symbols).

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Advances in Self-Organizing Maps: 8th International Workshop, WSOM 2011, Espoo, Finland, June 13-15, 2011. Proceedings by Barbara Hammer, Andrej Gisbrecht, Alexander Hasenfuss, Bassam Mokbel (auth.), Jorma Laaksonen, Timo Honkela (eds.)


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